UDA has been widely studied in computer vision mainly for classification and detection tasks. In the era of deep neural networks, the main idea of domain adaptation is to learn domain invariant features between the source and target domain. Among them, several methods exploit MMD [1]} and its kernel variants to minimize the difference in feature distribution.
With the rise of neural networks, [2]} attempts to introduce MMD as a regularization method to minimize the distribution mismatch between the source and target domain in the latent space. In addition to considering the adaptive algorithm of multi-feature representation, [3]} also provides an improved conditional maximum mean error.
